The paper proposes a new approach to processing sonar data for remote sensing by using CSV-format data instead of traditional image-based representations. Experiments demonstrate a 91.18% reduction in processing time, improved accuracy of machine‑learning object detection, and higher signal‑to‑noise and peak‑signal‑to‑noise ratios.
By Logan Luna, Sirio Jansen-S\'anchez, Ilteris Demirkiran, Leo Ghelarducci
arXiv:2506. 22174v3 Announce Type: replace-cross Abstract: The transport industry has recently shown significant interest in unmanned surface vehicles (USVs), specifically for port and inland waterway transport.
By Bavo Lesy, Siemen Herremans, Robin Kerstens, Jan Steckel, Walter Daems, Siegfried Mercelis, Ali Anwar
arXiv:2608. 07018v1 Announce Type: cross Abstract: Horizon detection in images of ice-covered waters is a challenging problem for maritime navigation due to low contrast between water and sky, cluttered ice structures, and varying illumination conditions.
By Alisa Pesotskaia, Emin Zerman
arXiv:2608.29852v1 Announce Type: new
Abstract: Automated maritime surveillance from satellite and aerial imagery requires large, precisely annotated datasets, which remain scarce for the instance-se...
By Amir Abbes, Ines Harrabi, Lucas Justin Yirepoa Kinda, Rim Trabelsi, Adnane Cabani, Fatma Abdelkefi
arXiv:2510. 04876v3 Announce Type: replace-cross Abstract: Benthic habitat mapping is fundamental for understanding marine ecosystems, guiding conservation efforts, and supporting sustainable resource management.
By Hayat Rajani, Valerio Franchi, Borja Martinez-Clavel Valles, Raimon Ramos, Rafael Garcia, Nuno Gracias
The paper investigates how raw or minimally processed satellite imagery affects onboard AI object detection for space missions. By systematically degrading Very High Resolution Maxar images in terms of Signal‑to‑Noise Ratio, Modulation Transfer Function, and Ground Sampling Distance, the authors evaluate three lightweight detectors—YOLOv5s, YOLOX‑S, and NanoDet—on the resulting data. Results show that image quality impacts detection performance in a degradation‑specific way, with GSD consistently shifting performance, while MTF and SNR effects vary by model and resolution; severe blur‑plus‑noise combinations cause the greatest losses.
By Adrien Dorise, Marjorie Bellizzi, St\'ephane May